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The Sleep Connectome

Advances in neuroscience are allowing visualization of sleep neural circuits.

//creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons
Source: By Andreashorn (Own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons

Advances in neuroscience often emerge when new methods of analyzing and visualizing the anatomy and function of the nervous system are developed. For example, the emerging field of neuroscience was greatly advanced by the development and use of stains that made it possible to visualize individual neurons in the nervous system. This in turn made it possible to begin to understand these fundamental components of neural networks. The study of the cognitive, motor, and emotional effects of various insults to the brain and nervous system such as head injuries, diseases such as multiple sclerosis, and damage due to strokes was advanced by the use of neuropsychological tests that helped to identify and quantify the effects of these injuries. The development of the electroencephalogram (EEG) in the 1920s helped scientists and physicians better understand the electrical activity of the cerebral cortex. This led to advances in the diagnosis of brain disorders such as epilepsy. Since the 1970s efforts have been made to use EEG patterns to understand the interconnections of neural centers in the cortex using a method referred to as Quantitative Electroencephalography (QEEG). The development of CT scanning, MRI, functional MRI, and other imaging techniques in the past several decades has greatly contributed to the growing understanding of the function of the nervous system by allowing visualization of brain structures in living organisms and by showing the functional activities of these centers. Using these methods, it is possible to create brain maps that relate anatomy and function.

What has emerged from this research is that the power of the brain does not reside in individual neural centers but in the synchronized activity of these centers. Systems of neurons perform the underlying tasks that are then coordinated and give rise to cognition, emotion and behavior.

In the early part of the last century it was believed that sleep occurred when stimulation from the senses stopped flowing to the cerebral cortex. This view of sleep as a passive process seemed to make sense as people and animals generally need to be safe and relaxed in a low stimulation environment to easily slip into sleep. Research starting in the 1940s gradually challenged this model. It is now recognized that sleep is actually a complex process and requires many neural systems to bring it about. It is not passive, but is indeed a very active and complex process. That it is so complex and must be so finely regulated means that a lot can go wrong with it, resulting in the various sleep disorders.

In day to day life there are three regularly occurring states of mental processing. These are wake, deep sleep, and REM sleep. The onset of sleep is based in the buildup of sleep drive over the course of the day and the regulating effects of the circadian rhythm. The alteration between deep sleep and REM sleep is regulated by complex brain systems that work to give the proper balance between them and lead to effective restoration of the nervous system and body so that the mind/body can operate at optimal levels during the day.

Within the brain there are pathways that allow interaction and coordination of neural systems in the front and back portions of the brain, the left and the right hemispheres, and the higher and lower centers. With the recognition of the importance of this interaction, increasing efforts have been made to trace the functional and structural components of the brain. The power of the nervous system resides in the complex coordination of the activities of huge numbers of individual neurons. This idea was extensively reviewed in the special section of the November 2013 issue of the journal Science, entitled “The Heavily Connected Brain” (See Markov et al, 2013; Park & Friston, 2013; Stern, 2013; Turk-browne, 2013). It is now possible to trace the connectivity between neurons and to use new analytical techniques such as network theory to understand the underlying mechanisms of structure and function in large neural networks. These methods are helping to understand how it is possible that a fixed structure such as the brain can give rise to so much functional diversity. While the brain is a fixed structure, it can be in diverse states such as wakefulness and dreaming. This is because of the varied and complex ways in which underlying neural pathways interact.

Connectomics is based in recent advances in mapping and analyzing neural networks. It is often compared to developments in genomics. A connectogram is a graphical visualization of the connections between areas of the brain revealed by diffusion MRI and analyzed with graph theory. It is typically depicted as a circle with interconnections drawn between representative areas on the circle that denote brain structures. You may have seen these circular diagrams in articles that show the strength of the relationships between various brain areas. A recent example was the widely reported finding of a study on the effects of the psychedelic drug psilocybin on brain functioning (Petri et al, 2014). In brief, the study found that psilocybin causes increased and different integration of certain brain areas as compared to the non-drugged state. This helps account for the profound mental states that this drug is reported to cause.

A connectome is a map of neural connections in the brain and shows connections mapped onto a representation of the brain. An example is the image at the top of this post. A visualization such as this is produced with a diffusion tensor image that uses functional magnetic resonance imaging to identify axon tracts by looking at the diffusion of water molecules in these tracts (Purves et al, 2012). One of the challenges of using these maps is that they are constantly changing, depending on the state and experiences of the organism. The connectome of a sleeping person in deep sleep will be different from that of an alert, focused, awake individual as the underlying neural systems interact in different ways based on these different states. Connectomes have been used to investigate the differences between male and female brains, positive and negative human traits, and are currently being investigated in a large scale research effort known as the Human Connectome Project that is being supported by the National Institutes of Health.

Recent work has begun to map the sleep connectome (Vyazovskly, 2015), initially looking at sleep in animals. This is helping to further elucidate the complex mechanisms that allow for the smooth transitions from wake to deep sleep to REM sleep. It is also helping to increase understanding of how sleep develops early in the life of animals, starting before birth. For example, a specific population of neurons in the hindbrain has been found that develops into the subpopulations of cells that ultimately contribute to the wake/sleep circuits (Hayashi, et al, 2015). This occurs very early in development before the states of wake and sleep have even emerged.

The Human Connectome Project, like the Human Genome Project before it, promises to greatly increase our understanding of the structure and function of the brain. I am especially excited by the possibility that it will help us better understand the ways in which the brain brings about and regulates states of consciousness such as wakefulness and sleep. Such understanding may well help in the development of more effective treatments of sleep disorders - something many of our sleep-challenged friends will appreciate!

Hayashi, Y., Kashiwagi, M., Yasuda, K., Ando, R., Kanuka, M., Sakai, K., & Itohara, S. (2015). Cells of a common developmental origin regulate REM/non-REM sleep and wakefulness in mice. Science, 20 November, 2015, 350 (6263), 957 – 961.

Markov, N.T., Ercsey-Ravasz, M., Van Essen, D.C., Knoblauch, K. Toroczkal, Z., & Kennedy, H. (2013). Cortical High-Density Counterstream Architectures. Science, 1 November, 2013, 342 (6158), p. 578.

Park, H-J, & Friston, K. (2013). Structural and Functional Brain Networks: From Connections to Cognition. Science, 1 November, 2013, 342 (6158), p. 579.

Petri G, Expert P, Turkheimer F, Carhart-Harris R, Nutt D, Hellyer PJ, Vaccarino F. (2014). Homological scaffolds of brain functional networks. J. R. Soc. Interface 11: 20140873. http://dx.doi.org/10.1098/rsif.2014.0873

Purves, D., Augustine, G. J., Fitzpatrick, D., Hall, W. C., LaMantia, A-S, White, L. E. (Eds.). (2012). Neuroscience 5th Edition, Sunderland, MA: Sinauer Associates, Inc.

Stern, P. (2013). Connection, connection, connection.... Science, 1 November, 2013, 342 (6158), p. 577.

Turk-Browne, N. B. (2013). Functional Interactions as Big Data in the Human Brain. Science, 1 November, 2013, 342 (6158), p. 580 – 584.

Vyazovskiy, V. V. (2015). Mapping the birth of the sleep connectome. Science, 20 November, 2015, 350 (6263), p. 909 – 910.

Source: "Yin and Yang" by Klem - This vector image was created with Inkscape by Klem, and then manually edited by Mnmazur.. Licensed under Public Domain via Wikimedia Commons -
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